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Evaluating tenant-landlord tensions using generative AI on online tenant forums

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Abstract

Tenant-landlord relationships exhibit a power asymmetry where landlords’ power to evict the tenants at a low-cost results in their dominating status in such relationships. Tenant concerns are thus often unspoken, unresolved, or ignored and this could lead to blatant conflicts as suppressed tenant concerns accumulate. Modern machine learning methods and Large Language Models (LLM) have demonstrated immense abilities to perform language tasks. In this study, we incorporate Latent Dirichlet Allocation with GPT-4 to classify Reddit post data scraped from the subreddit r/Tenant, aiming to unveil trends in tenant concerns while exploring the adoption of LLMs and machine learning methods in social science research. We find that tenant concerns in topics like fee dispute and utility issues are consistently dominant in all four states analyzed while each state has other common tenant concerns special to itself. Moreover, we discover temporal trends in tenant concerns that provide important implications regarding the impact of the pandemic and the Eviction Moratorium.

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Data Availability Statement

The data that support the findings of this study are not openly available. However, the same data can be accessed directly from Reddit through official or third-party APIs.

Notes

  1. Reddit Data API Update: Changes to Pushshift Access https://www.reddit.com/r/modnews/comments/134tjpe/reddit_data_api_update_changes_to_pushshift_access/.

  2. OpenAI API Reference Create chat completion https://platform.openai.com/docs/api-reference/chat/create.

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Authors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Xin Chen

  2. University at Albany, State University of New York, 135 Western Avenue, Albany, NY, USA

    Cheng Ren

  3. University of California, Berkeley, CA, USA

    Timothy A. Thomas

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Chen, X., Ren, C. & Thomas, T.A. Evaluating tenant-landlord tensions using generative AI on online tenant forums. J Comput Soc Sc 8, 50 (2025). https://doi.org/10.1007/s42001-025-00378-8

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